This repository documents a personal journey through the #100DaysOfMLCode challenge, providing a structured curriculum and practical examples across various machine learning domains. It serves as a learning resource for individuals aiming to build a foundational understanding and practical skills in ML and AI, with contributions from a growing community.

How It Works

The project follows a day-by-day breakdown of ML concepts, starting with fundamental data preprocessing techniques and progressing through regression, classification, clustering, association rules, reinforcement learning, NLP, deep learning, dimensionality reduction, and model selection. Each topic is illustrated with Python code examples, primarily using libraries like Pandas, Scikit-learn, and Matplotlib.

Quick Start & Requirements

• Install: Clone the repository and run Python scripts directly.
• Prerequisites: Python 3.x, NumPy, Pandas, Scikit-learn, Matplotlib, potentially libraries for specific topics like NLTK or TensorFlow/Keras for deep learning.
• Setup: Minimal setup required, primarily installing Python and core ML libraries.

Highlighted Details

• Comprehensive coverage of supervised, unsupervised, and reinforcement learning algorithms.
• Includes practical data preprocessing and visualization techniques.
• Covers advanced topics like Deep Learning (ANN, CNN) and Dimensionality Reduction.
• Features model selection and evaluation methods like Grid Search and K-fold Cross Validation.

Maintenance & Community

The project is actively maintained by the original author and a growing list of community contributors. Contributions are welcomed following provided guidelines.

Licensing & Compatibility

Licensed under the MIT License, permitting commercial use and modification with attribution.

Limitations & Caveats

This repository represents a personal learning log; code examples may require adaptation for specific datasets or production environments. Some advanced topics might have simplified implementations.